hlmhmm metacognition seed selection and model comparison

library(reticulate)
use_condaenv("glmhmm")

ERROR: The requested version of Python
('/Users/daniellinares/opt/anaconda3/envs/glmhmm/bin/python')
cannot be used, as another version of Python
('/Library/Frameworks/Python.framework/Versions/3.10/bin/python3')
has already been initialized. Please restart the
R session if you need to attach reticulate to a
different version of Python.
Error in use_python(python, required = required) : 
  failed to initialize requested version of Python

import numpy as np
import numpy.random as npr
import matplotlib.pyplot as plt
import ssm 

library(tidyverse)

── Attaching core tidyverse packages ───────────────────
✔ dplyr     1.1.2     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.2     ✔ tibble    3.2.1
✔ lubridate 1.9.2     ✔ tidyr     1.3.0
✔ purrr     1.0.1     ── Conflicts ────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors

library(broom)
library(dplyr)
library(R.matlab)

R.matlab v3.7.0 (2022-08-25 21:52:34 UTC) successfully loaded. See ?R.matlab for help.

Attaching package: ‘R.matlab’

The following objects are masked from ‘package:base’:

    getOption, isOpen

library(cowplot)

Attaching package: ‘cowplot’

The following object is masked from ‘package:lubridate’:

    stamp

list.files("R", full.names = TRUE) |> 
  walk(source)

load("RData/glm_hmm1_seeds.RData")
load("RData/glm_hmm2_seeds.RData")
load("RData/glm_hmm3_seeds.RData")

Renames the states according to their values (so similar states between subjects can be identified)

# glm_hmm1 <- glm_hmm1_seeds %>% 
#     mutate(fit = list(glm_hmm_extract_best(fit))) %>%
#     order_states_glm_hmm()
# 
# glm_hmm1
# 
# save(glm_hmm1, file = "RData/glm_hmm1.RData")

# glm_hmm3 <- glm_hmm3_seeds %>%
#   mutate(fit = list(glm_hmm_extract_best(fit))) %>%
#   order_states_glm_hmm()
# 
# glm_hmm3
# 
# save(glm_hmm3, file = "RData/glm_hmm3.RData")

glm_hmm4 <- glm_hmm4_seeds %>%
  mutate(fit = list(glm_hmm_extract_best(fit))) %>%
  order_states_glm_hmm()

Joining with `by = join_by(subject, state)`

load("RData/glm_hmm1.RData")
load("RData/glm_hmm2.RData")
load("RData/glm_hmm3.RData")

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MODEL COMPARISON

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liks de cada modelo

DL: